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This study investigates the structure of factors that influence consumer intentions to both try and to consume cultured proteins, and their intentions to substitute vegan, vegetarian and omnivore diets with these alternative protein sources. Comprehensive survey data (N = 3862) was collected from three Nordic countries (Denmark, Finland, and Norway) and analysed using confirmatory factor analysis and structural equation modelling. Theoretically, this article draws from behavioural models of environmental psychology, identity theory, and attitude theory. Results indicate that beliefs about the necessity of an industry producing cultured proteins and impacts of cultured proteins on the global economy are significant predictors of consumer intentions. Moreover, participants who exhibited high levels of general and food innovativeness were more likely to express positive intentions to consume cultured proteins. Social norms influenced consumer intentions: Individuals surrounded by positive attitudes and intentions toward cultured proteins within their social networks were more inclined to want to consume these products. The predictor variables in the final model accounted for between 39% and 66% of the variance in the different cultured proteins related intentions. Understanding consumer intentions better can inform targeted communication strategies aimed at promoting the advantages of cultured proteins and facilitating its adoption. [ABSTRACT FROM AUTHOR]

Abstract: Probabilistic risk assessment is gaining acceptance as the most appropriate way to characterize and communicate uncertainties in estimates of human health risk and/or reference levels of exposure such as benchmark doses. Although probabilistic techniques are well established in the exposure-assessment component of the National Research Council’s risk-assessment paradigm, they are less well developed in the dose–response-assessment component. This paper proposes the use of hierarchical statistical models as tools for implementing probabilistic dose–response assessments, in that such models provide a natural connection between the pharmacokinetic (PK) and pharmacodynamic (PD) components of dose–response models. The results show that incorporating internal dose information into dose–response assessments via the coupling of PK and PD models in a hierarchical structure can reduce the uncertainty in the dose–response assessment of risk. However, information on the mean of the internal dose distribution is sufficient; having information on the variance of internal dose does not affect the uncertainty in the resulting estimates of excess risks or benchmark doses. In addition, the complexity of a PK model of internal dose does not affect how the variability in risk is measured via the ultimate endpoint. [Copyright &y& Elsevier]